Emotion decoding from visually evoked human brain activity measured by functional Magnetic Resonance Imaging (fMRI) is an emerging research area and plays an important role in Brain-Computer Interfaces (BCIs). Most of the previous emotion decoding studies [1]}, [2]}, [3]} considered very coarse-grained and limited emotion categories, such as positive, neutral and negative, which can not represent the complex emotions we experience in daily life. Recent studies [4]}, [5]} have proven that the emotions expressed by human constitute a high-dimensional semantic space which motivates us to study more fine-grained emotion decoding. In addition, a more obvious limitation in previous decoding methods is that they only predicted one emotion category for one brain activity pattern [2]}, [3]}, [8]}, which means they only regard emotion decoding as a multi-class, rather than multi-label, decoding problem. However, there is no doubt that multiple human emotions can be elicited simultaneously by emotion stimuli. Fig. REF  shows three video screenshots of emotion-inducing movie clips used in our experimental dataset. Taking the third scene fighting as an example, we may feel angry and disgust for this uncivilized behavior while we are also surprised and confused with the reason for fighting. Besides, it is fearful and anxious for us when we see people fighting on the street, which will make us sad about the injury as well.
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